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babelnet-1.1.0
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Python API for BabelNet
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | babelnet-1.1.0 |
| نام | babelnet |
| نسخه کتابخانه | 1.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Babelscape |
| ایمیل نویسنده | info@babelscape.com |
| آدرس صفحه اصلی | https://babelnet.org/ |
| آدرس اینترنتی | https://pypi.org/project/babelnet/ |
| مجوز | - |
This package consists of a Python API to work with BabelNet, a very large multilingual semantic network. For more
information, please refer to the documentation below on how to use the software, and our
website (https://babelnet.org) for news, updates and papers.
# Version compatibility
BabelNet Python API can be used with BabeNet 4.0 and above.
# Configuration
After the installation, the first step to take when you want to use BabelNet in another project (or in the REPL) is to
create a file called `babelnet_conf.yml` in the current working directory.
Alternatively, the path of the configuration file can be specified using the `BABELNET_CONF` environment variable.
The content of the `babelnet_conf.yml` should vary according to the usage mode of choice:
**Online Mode**: uses the online REST service to retrieve the data. To use this mode you need an internet connection
and a [valid API key](https://babelnet.org/guide#access).
**RPC Mode**: reads data directly from a local copy of the BabelNet indices, making it more suitable for heavy
workloads than the online mode since it is faster and doesn't have usage limits. To use this mode you need the
[BabelNet indices](https://babelnet.org/downloads) and [Docker installed in your system](https://www.docker.com/get-started/).
The *RPC server controller* (see below) requires additional dependencies that can be installed with the following pip command:
```bash
pip install babelnet[rpc]
```
Further details on how to use these modes are provided in the following sections.
## Online Mode
This is the simplest version to use, since it requires only [a valid API key](https://babelnet.org/guide#access).
However, the drawback is that the iterators are unavailable, i.e. the `iterator`, `offset_iterator`, `lexicon_iterator`
and `wordnet_iterator` methods.
Assuming you have received by e-mail the key `3x54mp13-8au0-o97q-9vzz-3vakcpec8w4p`, add the following line
to `babelnet_conf.yml`:
```yaml
RESTFUL_KEY: '3x54mp13-8au0-o97q-9vzz-3vakcpec8w4p'
```
This will automatically be used to authenticate you on the official BabelNet REST service.
The supported REST endpoints are:
- `https://babelnet.io/v8/service` for BabelNet 5.2 *(default)*
- `https://babelnet.io/v7/service` for BabelNet 5.1
- `https://babelnet.io/v6/service` for BabelNet 5.0
- `https://babelnet.io/v5/service` for BabelNet 4.0
If you want to use a different REST endpoint, add the following line to `babelnet_conf.yml`:
```yaml
# BabelNet 5.2 REST endpoint
RESTFUL_URL: 'https://babelnet.io/v8/service'
```
## RPC Mode
To use the RPC mode you need a **local copy of the BabelNet indices**. To download them, follow the procedure
on the [official website](https://babelnet.org/downloads).
This can be considered a _full_ mode, because it has no usage limit and faster responses.
BabelNet Python API requires PyLucene, which has a dependency on Lucene itself. The installation process of Lucene can be tricky
since it has many dependencies that need compiling. Because of this, we moved this PyLucene build and install process to
a simple Docker image. In the RPC mode, the Remote Procedure Call paradigm is applied in calling this Docker container
as a remote service, effectively decoupling PyLucene and BabelNet.
To configure the APIs in RPC mode, you just need to add one of these lines to your `babelnet_conf.yml`, depending on
which protocol you want to use.
The default protocol used by the RPC server is TCP. You can specify the URL where the server is listening with the
following configuration line.
```yaml
# TCP URL example
RPC_URL: "tcp://127.0.0.1:7790"
```
If the RPC server has the optional IPC protocol enabled, you can use it with the following configuration line.
```yaml
# IPC URL example
RPC_URL: "ipc:///home/user/your_ipc_dir/socket"
```
*__Important__:* to use lambdas in RPC mode, the client code must be run using the **same Python version** of the
server, i.e. Python 3.8, and the **same (or older) version of cloudpickle**, i.e. 2.1.0.
To start the server, you can either use the *RPC server controller* or manually start the Docker.
In any case you need [Docker to be installed in your system](https://www.docker.com/get-started/).
The controller is described in the following section; for details on how to directly use the
Docker image, please follow the documentation on the [Docker Hub page](https://hub.docker.com/r/babelscape/babelnet-rpc).
***Note:*** when you update the API to a newer version, you need to either restart the server using the controller or
pull the new docker from the hub and start a new server with the updated image.
# RPC server controller
To simplify the management of the RPC server, you can use the `babelnet-rpc` command.
The additional dependencies required by the controller can be installed with the command:
```bash
pip install babelnet[rpc]
```
***For Windows users:*** if you are working in an Anaconda environment, you need to install `pywin32` using anaconda
with the following command:
```bash
conda install pywin32=227
```
## Documentation
Once the server is started, the documentation of the Python API will be available at
[http://localhost:7780](http://localhost:7780),
or alternatively to the port defined by the arguments of the `start` command.
## Start the server
To start the server, you can use the command `babelnet-rpc start`.
If no arguments are provided, it will start in interactive mode, in which you will be prompted to provide the required
values.
```console
$ babelnet-rpc start
BabelNet indices path: /home/user/BabelNet-5.2
Port for documentation ([7780], -1 to ignore): 8080
RPC mode ([tcp]/ipc/all): all
Port for TCP mode ([7790]):
IPC directory: your_ipc_dir
Starting server...
Server started
BabelNet Python API documentation is available at http://localhost:8080
To use BabelNet in RPC mode, add one of these lines in your babelnet_conf.yml file
RPC_URL: "tcp://127.0.0.1:7790"
RPC_URL: "ipc:///home/user/your_ipc_dir/socket"
```
Alternatively, the values can be passed as arguments. The available arguments are:
- `--bn <path>` required, the BabelNet indices path
- `--doc <port>` port for the BabelNet API documentation (default `7780`)
- `--no-doc` disable the documentation port
- `-m`, `--mode` the RPC mode enabled on the server (`tcp`, `ipc` or `all`, default `tcp`). _On Windows the only
available mode is `tcp`_.
- `--tcp <port>` the port for TPC mode (default `7790`)
- `--ipc <path>` the IPC directory (required with mode `ipc` or `all`)
- `--print` print the command instead of executing it
### Examples of usage
#### Basic usage
```console
$ babelnet-rpc start --bn /home/user/BabelNet-5.2
Starting server...
Server started
BabelNet Python API documentation will be available at http://localhost:7790
To use BabelNet in RPC mode, add this line in your babelnet_conf.yml file
RPC_URL: "tcp://127.0.0.1:7790"
```
#### IPC mode without documentation
```console
$ babelnet-rpc start --bn /home/user/BabelNet-5.2 --no-doc -m ipc --ipc your_ipc_dir
Starting server...
Server started
To use BabelNet in RPC mode, add this line in your babelnet_conf.yml file
RPC_URL: "ipc:///home/user/your_ipc_dir/socket"
```
#### Custom TCP port, print docker command
```console
$ babelnet-rpc start --bn /home/user/BabelNet-5.2 --print --tcp 1234
To start the RPC server, run the following command:
docker run -d --name babelnet-rpc -p 7780:8000 -p 1234:1234 -v "/home/user/BabelNet-5.2:/root/babelnet" babelscape/babelnet-rpc:latest
BabelNet Python API documentation will be available at http://localhost:7780
To use BabelNet in RPC mode, add this line in your babelnet_conf.yml file
RPC_URL: "tcp://127.0.0.1:1234"
```
## Stop the server
To stop a running RPC server, run the command:
```bash
babelnet-rpc stop
```
# Code
Assuming the installation and configuration phases have been completed, you can start working with BabelNet.
The entry point in the library is the `babelnet` package. It contains a set of functions that query the available
content. You can import the package by calling:
```python
import babelnet as bn
```
The two main classes of BabelNet are:
- `BabelSynset` (a concept or named entity identified by a set of multilingual lexicalizations, each being a
BabelSense)
- `BabelSense` (a lexicalization of a given concept, i.e. a BabelSynset)
For more details, see the API documentation at
[https://babelnet.org/pydoc/1.1/](https://babelnet.org/pydoc/1.1/).
## BabelSynset
A `BabelSynset` is a set of multilingual lexicalizations that are synonyms expressing a given concept or named entity.
For instance, the synset for car in the motorcar sense
looks [like this](https://babelnet.org/synset?word=bn:00007309n&details=1&orig=car&lang=EN). After importing `babelnet`
as `bn` we can use its functions to retrieve one or many `BabelSynset` objects. For instance, to retrieve all the
synsets containing `car` we can call `get_synsets`:
```python
from babelnet.language import Language
# Given a word in a certain language,
# returns the concepts (BabelSynsets) denoted by the word.
byl = bn.get_synsets('car', from_langs=[Language.EN])
```
We can also specify which of the parts of speech we are interested in and obtain only synsets for the specified part of
speech. In the following example, we retrieve all the verbal synsets containing the English lexicalization `run` :
```python
from babelnet.language import Language
from babelnet.pos import POS
# Given a word in a certain language and pos (part of speech),
# returns the concepts denoted by the word.
byl = bn.get_synsets('run', from_langs=[Language.EN], poses=[POS.VERB])
```
Due to the [nature of BabelNet](https://babelnet.org/about), a `BabelSynset` may contain lexicalizations from different
sources. You can restrict your search only to your sources of interest. For instance:
```python
from babelnet.language import Language
from babelnet.pos import POS
from babelnet.data.source import BabelSenseSource
# Given a word in a certain language, returns the concepts
# for the word available in the given sense sources.
byl = bn.get_synsets('run', from_langs=[Language.EN], poses=[POS.NOUN],
sources=[BabelSenseSource.WIKI, BabelSenseSource.OMWIKI])
```
Each `BabelSynset` has an ID that univocally identifies the synset, and that can be obtained via the `id`
attribute of BabelSynset instances. If we have an ID and want to retrieve the corresponding synset, we can
use `get_synset`. For instance:
```python
from babelnet.resources import BabelSynsetID
# Gets a BabelSynset from a concept identifier (Babel synset ID).
by = bn.get_synset(BabelSynsetID('bn:03083790n'))
```
returns the BabelSynset corresponding to
ID [bn:03083790n](https://babelnet.org/synset?id=bn%3A03083790n&orig=bn%3A03083790n&lang=EN), that is, the synset about BabelNet.
If we want to retrieve the BabelSynset corresponding to a given WordNet 3.0 ID, we can do the following:
```python
from babelnet.resources import WordNetSynsetID
# Gets the BabelSynsets corresponding to an input WordNet offset.
by = bn.get_synset(WordNetSynsetID('wn:06879521n'))
```
If we want to retrieve the BabelSynset corresponding to a given Wikidata page ID, we can do the following:
```python
from babelnet.resources import WikidataID
# Gets the BabelSynsets corresponding to an input Wikidata page ID.
by = bn.get_synset(WikidataID('Q4837690'))
```
If we want to retrieve the BabelSynsets containing a given Wikipedia page title, we can use the function `get_synsets`:
```python
from babelnet.language import Language
from babelnet.pos import POS
from babelnet.resources import WikipediaID
# Given a Wikipedia title, returns the BabelSynsets which contain it.
byl = bn.get_synsets(WikipediaID('Men in Black (film 1997)', Language.IT, POS.NOUN))
```
## BabelSense
A `BabelSense` is a term (either word or multi-word expression) in a given language occurring in a certain `BabelSynset`
. Each occurrence of the same term (e.g., car) in different synsets is, therefore, a different `BabelSense` of that term.
Now let's look at the functions to retrieve a `BabelSense` using the `bn` module we have imported earlier:
```python
from babelnet.language import Language
from babelnet.pos import POS
from babelnet.data.source import BabelSenseSource
# Returns the senses for the word in a certain language.
senses1 = bn.get_senses('run', from_langs=[Language.EN])
# Returns the senses for the word in a certain language and Part-Of-Speech.
senses2 = bn.get_senses('run', from_langs=[Language.EN], poses=[POS.VERB])
# Returns the senses for the word with the given constraints.
senses3 = bn.get_senses('run', from_langs=[Language.EN], poses=[POS.VERB],
sources=[BabelSenseSource.WIKI, BabelSenseSource.OMWIKI])
```
Once we have a `BabelSense`, we can go back to the synset it belongs with the `synset` property:
```python
by = sense.synset
```
We can view the `BabelSynset` as a container of `BabelSense` s, i.e., the lexicalizations in the various languages
contained in the synset that express its concept or named entity.
## Some attributes of BabelSynset and BabelSense
We are now going into details about important attributes (methods, properties)
of the `BabelSynset` and `BabelSense` classes.
### BabelSynset
`BabelSynset` is composed of various elements, which we describe below. Furthermore, a `BabelSynset` is connected to
other `BabelSynset` objects. The main components of a `BabelSynset` are objects of the following types:
1. `BabelSense` (a lexicalization of the concept, see above)
2. `POS` (the synset's part of speech)
3. `BabelGloss` (a definition of the concept in a given language)
4. `BabelExample` (an example sentence of the meaning expressed by the synset)
5. `BabelImage` (an image depicting the concept)
6. `BabelSynsetRelation` (an edge semantically connecting the synset to another synset)
Let's take a look at the main methods and properties of a `BabelSynset` object which we call `by`. *Note:* to
obtain `BabelSynset` objects we can also use the above examples.
```python
# Get a BabelSynset from a concept identifier (Babel synset ID).
by = bn.get_synset(BabelSynsetID('bn:03083790n'))
# Most relevant BabelSense to this BabelSynset for a given language.
bs = by.main_sense(Language.EN)
# The part of speech of this BabelSynset.
pos = by.pos
# True if the BabelSynset is a key concept
is_key_concept = by.is_key_concept
# Gets the senses contained in this BabelSynset.
senses = by.senses()
# Collects all BabelGlosses in the given source for this BabelSynset.
glosses = by.glosses()
# Collects all BabelExamples for this BabelSynset.
examples = by.examples()
# The images (BabelImages) of this BabelSynset.
images = by.images
# Collects all the edges incident on this BabelSynset.
edges = by.outgoing_edges()
# Gets the BabelCategory objects of this BabelSynset.
cats = by.categories()
```
### BabelSense
We now have a look at the BabelSense attributes. The main components of a BabelSense are:
1. `BabelSynset` (the synset the sense belongs to)
2. `POS` (its part-of-speech tag)
3. the lemma string (the lexicalization of the sense)
4. `BabelSensePhonetics` (the written and audio pronunciations of this sense)
5. `BabelSenseSource` (the source of the sense, e.g.: Wikipedia, WordNet, etc.)
Some code retrieving the above information follows:
```python
bs = by.main_sense(Language.EN)
# The language of this BabelSense
lang = bs.language
# The part-of-speech tag of this BabelSense
pos = bs.pos
# True if the BabelSense is a key concept
is_key_concept = bs.is_key_sense
# The lemma of this BabelSense
lemma = bs.full_lemma
# The normalized lemma of this sense (i.e., lowercase, without parentheses, etc.)
normalized_lemma = bs.normalized_lemma
# The pronunciations of this sense
pronunciations = bs.pronunciations
# The source of the sense; ex: Wikipedia, WordNet, etc.
source = bs.source
```
# Usage examples
Here we show full examples that show how you can use the BabelNet API to accomplish several tasks.
## Retrieve all BabelSynset objects for a specific word
```python
import babelnet as bn
from babelnet import Language
for synset in bn.get_synsets('home', from_langs=[Language.EN]):
print('Synset ID:', synset.id)
```
## For a specific word retrieves all BabelSynset objects in English, Italian and French
```python
import babelnet as bn
from babelnet import Language
synsets = bn.get_synsets('home', from_langs=[Language.EN],
to_langs=[Language.IT, Language.FR])
for synset in synsets:
print('Synset ID:', synset.id)
```
## Retrieve all BabelSense objects for a specific BabelSynset object
```python
import babelnet as bn
from babelnet import BabelSynsetID
synset = bn.get_synset(BabelSynsetID('bn:00000356n'))
# a synset is an iterator over its senses
for sense in synset:
print('Sense: ' + sense.full_lemma,
'Language: ' + str(sense.language),
'Source: ' + str(sense.source), sep='\t')
phonetic = sense.pronunciations
for audio in phonetic.audios:
print('Audio URL', audio.validated_url)
```
## Retrieve all BabelSense objects for a specific Wikidata page id
```python
import babelnet as bn
from babelnet.resources import WikidataID
synset = bn.get_synset(WikidataID('Q4837690'))
# a synset is an iterator over its senses
for sense in synset:
print('Sense: ' + sense.full_lemma,
'Language: ' + str(sense.language),
'Source: ' + str(sense.source), sep='\t')
phonetic = sense.pronunciations
for audio in phonetic.audios:
print('Audio URL', audio.validated_url)
```
## Retrieve Wikidata id for each BabelSense in a BabelSynset
```python
import babelnet as bn
from babelnet import BabelSynsetID, BabelSenseSource
by = bn.get_synset(BabelSynsetID('bn:00000356n'))
for sense in by.senses(source=BabelSenseSource.WIKIDATA):
sensekey = sense.sensekey
print(sense.full_lemma, sense.language, sensekey, sep='\t')
```
## Retrieve neighbors of a BabelSynset object
```python
import babelnet as bn
from babelnet import BabelSynsetID, Language
from babelnet.data.relation import BabelPointer
by = bn.get_synset(BabelSynsetID('bn:00015556n'))
for edge in by.outgoing_edges(BabelPointer.ANY_HYPERNYM):
print(str(by.id) + '\t' + by.main_sense(Language.EN).full_lemma,
edge.pointer, edge.id_target, sep=' - ')
```
## Retrieve the distribution of relationships (frequency of each BabelPointer type) for a specific word
```python
from itertools import groupby
import babelnet as bn
from babelnet import Language
synsets = bn.get_synsets('car', from_langs=[Language.EN])
li = [edge.pointer.symbol for synset in synsets for edge
in synset.outgoing_edges()]
for p, l in groupby(sorted(li)):
print(p, len(list(l)), sep='\t')
```
# Multithreading
In **online mode** requests can come from different threads or processes and are elaborated concurrently.
In **RPC mode**, using the API simultaneously from multiple threads is discouraged due to Python's threading
management and the limitations of the RPC library. Since sending concurrent requests to the server can lead to long
response times, to avoid timeouts it is recommended to use a limited pool like in the following example.
```python
import concurrent.futures
from datetime import datetime
from sys import stdout
import babelnet as bn
from babelnet import Language
# function called from the threads
def func(name: str, word: str):
stdout.write(datetime.now().strftime("%H:%M:%S.%f") + " - Start - " + name + "\n")
synsets = bn.get_synsets(word, from_langs=[Language.EN])
glosses = []
for synset in synsets:
gloss = synset.main_gloss(Language.EN)
if gloss:
glosses.append(gloss.gloss)
stdout.write(datetime.now().strftime("%H:%M:%S.%f") + " - End - " + name + "\n")
return {word: glosses}
word_list = ["vocabulary", "article", "time", "bakery", "phoenix", "stunning", "judge", "clause", "anaconda",
"patience", "risk", "scribble", "writing", "zebra", "trade"]
with concurrent.futures.ThreadPoolExecutor(max_workers=8) as executor:
future = []
for i, w in enumerate(word_list):
future.append(executor.submit(func, f'Thread {i} "{w}"', w))
results = {}
for f in future:
results.update(f.result())
for w, gs in results.items():
for g in gs:
print(w, g, sep='\t')
```
# Authors
Babelscape
([info@babelscape.com](mailto:info@babelscape.com))
# License
**BabelNet** and its **API** are licensed under the [BabelNet Non-Commercial License](https://babelnet.org/license).
نیازمندی
| مقدار | نام |
|---|---|
| - | cloudpickle |
| - | zerorpc |
| !=23.0.*,>=22.3 | pyzmq |
| - | dataclasses |
| >=1.2 | text-unidecode |
| >=3.0.1 | ordered-set |
| >=2.19.1 | requests |
| >=4.2b4 | pyyaml |
| >=2.1.2 | aenum |
| - | docker |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| ==3.8.* | Python |
نحوه نصب
نصب پکیج whl babelnet-1.1.0:
pip install babelnet-1.1.0.whl
نصب پکیج tar.gz babelnet-1.1.0:
pip install babelnet-1.1.0.tar.gz